Data collection and the availability of large data sets has increased over the last decades. In both statistical and machine learning frameworks, two methodological issues typically arise when performing regression analysis on large data sets. First, variable selection is crucial in regression...

How risky are investments in residential real estate? To answer this question, information is needed about the behavior of house prices. The hedonic methodology has become a standard approach for modelling the prices of heterogeneous assets. Although intuitively appealing, it is often criticized...

When alternatives are compared using an estimated criterion function, this may introduce a discrepancy between the true and the estimated criterion. In this paper, we consider a situation where a preordering (ranking) of stochastic sequences is defined from expected loss/gain, using a parametric...

This dissertation consists of five independent projects. In each project, a novelstatistical method was developed to address a practical problem encountered in genomiccontexts. For example, we considered testing for constant nonparametric effectsin a general semiparametric regression model in...

An important challenge in statistical modeling involves determining an appropriate structural form for a model to be used in making inferences and predictions. Missing data is a very common occurrence in most research settings and can easily complicate the model selection problem. Many useful...